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DATE
2008
IEEE
136views Hardware» more  DATE 2008»
14 years 2 months ago
A Framework of Stochastic Power Management Using Hidden Markov Model
- The effectiveness of stochastic power management relies on the accurate system and workload model and effective policy optimization. Workload modeling is a machine learning proce...
Ying Tan, Qinru Qiu
PR
2010
147views more  PR 2010»
13 years 6 months ago
Minimum classification error learning for sequential data in the wavelet domain
Wavelet analysis has found widespread use in signal processing and many classification tasks. Nevertheless, its use in dynamic pattern recognition have been much more restricted ...
D. Tomassi, Diego H. Milone, L. Forzani
EMMCVPR
2001
Springer
14 years 4 days ago
Designing the Minimal Structure of Hidden Markov Model by Bisimulation
Hidden Markov Models (HMMs) are an useful and widely utilized approach to the modeling of data sequences. One of the problems related to this technique is finding the optimal stru...
Manuele Bicego, Agostino Dovier, Vittorio Murino
CVPR
2004
IEEE
13 years 11 months ago
Modeling Complex Motion by Tracking and Editing Hidden Markov Graphs
In this paper, we propose a generative model for representing complex motion, such as wavy river, dancing fire and dangling cloth. Our generative method consists of four component...
Yizhou Wang, Song Chun Zhu
BMCBI
2006
102views more  BMCBI 2006»
13 years 7 months ago
Protein secondary structure prediction for a single-sequence using hidden semi-Markov models
Background: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algor...
Zafer Aydin, Yucel Altunbasak, Mark Borodovsky